SUMO‐Mimicking Peptides Inhibiting Protein SUMOylation

The ubiquitin‐like protein SUMO is transferred through a core E1–E2 cascade composed of the SUMO‐activating enzyme (SAE) and Ubc9 to modify cellular proteins and transmit important biological signals. SAE primarily recognizes the C‐terminal tail of SUMO and catalyzes ATP condensation with the SUMO C‐terminal carboxylate to activate its transfer through the cascade. Here, we used phage display to show that a broad profile of SUMO C‐terminal sequences could be activated by SAE. Based on this, we developed heptamer peptides that could 1) form thioester conjugates with SAE, 2) be transferred from SAE to Ubc9, and 3) be further transferred to the SUMOylation target protein RanGAP1. As these peptides recapitulate the action of SUMO in protein modification, we refer to them as “SUMO‐mimicking peptides”. We found that, once the peptides were conjugated to SAE and Ubc9, they blocked full‐length SUMO from entering the cascade. These peptides can thus function as mechanism‐based inhibitors of the protein SUMOylation reaction.     

基于SUMO的路由协议仿真研究

利用TIGER数据库,构建一个实际道路地图作为仿真场景,借助SUMO交通仿真器和NS2网络仿真平台,评估ADOV、DSR、DSDR 3种路由协议在城市场景车载自组网(VANET)中的适用性.实验结果表明,上述3种协议在城市VANET环境下,存在分组传输成功率低、归一化路由负载高、平均端到端延时大的缺点,难以满足现有城市...     

Profiling Substrate Specificity of the SUMO Protease Ulp1 by the YESS–PSSC System to Advance the Conserved Mechanism for Substrate Cleavage

SUMO modification is a vital post-translational regulation process in eukaryotes, in which the SUMO protease is responsible for the maturation of the SUMO precursor and the deconjugation of the SUMO protein from modified proteins by accurately cleaving behind the C-terminal Gly–Gly motif. To promote the understanding of the high specificity of the SUMO protease against the SUMO protein as well as to clarify whether the conserved Gly–Gly motif is strictly required for the processing of the SUMO precursor, we systematically profiled the specificity of the S. cerevisiae SUMO protease (Ulp1) on Smt3 at the P2–P1↓P1’ (Gly–Gly↓Ala) position using the YESS–PSSC system. Our results demonstrated that Ulp1 was able to cleave Gly–Gly↓ motif-mutated substrates, indicating that the diglycine motif is not strictly required for Ulp1 cleavage. A structural-modeling analysis indicated that it is the special tapered active pocket of Ulp1 conferred the selectivity of small residues at the P1–P2 position of Smt3, such as Gly, Ala, Ser and Cys, and only which can smoothly deliver the scissile bond into the active site for cleavage. Meanwhile, the P1’ position Ala of Smt3 was found to play a vital role in maintaining Ulp1’s precise cleavage after the Gly–Gly motif and replacing Ala with Gly in this position could expand Ulp1 inclusivity against the P1 and P2 position residues of Smt3. All in all, our studies advanced the traditional knowledge of the SUMO protein, which may provide potential directions for the drug discovery of abnormal SUMOylation-related diseases.     

"基于城市雨洪模型的内涝交通影响研究

随着城市化进程的加快以及极端天气的频繁出现,城市内涝问题日益突出。交通损失为超标暴雨积水造成的主要损失之一。从暴雨内涝积水原理及交通通行规律的研究入手,综合运用气象水文、交通、气象、遥感、地理、灾害等学科的理论,以北京典型桥区为研究对象,基于高分辨率的DEM地形资料和降水、土地利用数据,构建典型桥区的一维雨洪模型,采用函数拟合的溢流量—积水深度曲线法模拟道路实际积水过程;耦合桥区一维雨洪模型与交通仿真模型,定量研究了内涝积水对交通系统运行状态的影响度。结果表明,暴雨积水导致的车辆减速、绕行会大大降低交通系统的运行效率。研究成果可为暴雨条件下的交通影响分析提供参考。     

基于注意力机制的深度强化学习交通信号控制

深度强化学习（DRL）广泛应用于具有高度不确定性的城市交通信号控制问题中,但现有的DRL交通信号控制方法中,仅仅使用传统的深度神经网络,复杂交通场景下其感知能力有限。此外,状态作为强化学习的三要素之一,现有方法中的交通状态也需要人工精心的设计。因此,提出了一种基于注意力机制（attention mechanism）的DRL交通信号控制算法。通过引入注意力机制,使得神经网络自动地关注重要的状态分量以增强网络的感知能力,提升了信号控制效果,并减少了状态向量设计的难度。在SUMO（simulation of urban mobility）仿真平台上的实验结果表明,在单交叉口、多交叉口中,在低、高交通流量条件下,仅仅使用简单的交通状态,与三种基准信号控制算法相比,所提算法在平均等待时间、行驶时间等指标上都具有最好的性能。     

一种基于分类回归树的无人车汇流决策方法

决策规划是无人驾驶技术中的重要环节.由于道路结构变化或障碍物引起的车辆被动换道多采用基于逻辑规则或优化算法的决策方式.本文以通行量为优化目标,提出一种基于分类回归树（Classification and regression tree,CART）的汇流决策方法.依据交通流参数,选择大量具有代表性的车辆汇流场景.对场景中车辆的汇流决策序列进行编码,采用遗传算法搜索使得通行量最大的决策方案.将寻优获得的大量汇流决策序列作为样本,训练分类回归树.选取车辆自身信息及与周围车辆的关系等以描述环境特征,运用分类回归树描述环境特征与决策结果的映射关系,获得一种通行量最优的汇流决策方法.在软件中进行仿真实验,对比既有方法,基于分类回归树的汇流方法能够有效减少汇流行为对车流的扰动,在大流量情形下依旧能保持较高的通行效率.此外,该方法对实际实施中可能存在的环境感知误差,如定位误差,有一定的鲁棒性.     

上位本体中语义相似度的计算及其实现

基于SUMO(Suggested Upper Merged Ontology)[1],提出一种计算两概念语义相似度的语义距离方法.根据该方法实现一个计算平台,将计算结果同人类的主观判断进行比较,验证概念语义相似度计算方法的有效性.研究成果拟在正在研发的语义数据库中本体集成部分得到应用,也可以为本体的其它相关研究提供一定的技术方法基础.     

DOLCE ergo SUMO: On foundational and domain models in the SmartWeb Integrated Ontology (SWIntO)

Increased availability of mobile computing, such as personal digital assistants (PDAs), creates the potential for constant and intelligent access to up-to-date, integrated and detailed information from the Web, regardless of one's actual geographical position. Intelligent question-answering requires the representation of knowledge from various domains, such as the navigational and discourse context of the user, potential user questions, the information provided by Web services and so on, for example in the form of ontologies. Within the context of the SmartWeb project, we have developed a number of domain-specific ontologies that are relevant for mobile and intelligent user interfaces to open-domain question-answering and information services on the Web. To integrate the various domain-specific ontologies, we have developed a foundational ontology, the SmartSUMO ontology, on the basis of the DOLCE and SUMO ontologies. This allows us to combine all the developed ontologies into a single SmartWeb Integrated Ontology (SWIntO) having a common modeling basis with conceptual clarity and the provision of ontology design patterns for modeling consistency. In this paper, we present SWIntO, describe the design choices we made in its construction, illustrate the use of the ontology through a number of applications, and discuss some of the lessons learned from our experiences.     

基于SUMO的概念语义相似度研究

SUMO（建议上层共享知识本体）是由IEEE标准上层知识本体工作小组所建置的,其目的是发展标准的上层知识本体,这将促进数据互通性、信息搜寻和检索、自动推理和自然语言处理。基于该共享知识本体,提出了一种计算两概念语义相似度的方法。根据该方法实现了一个计算程序模块,并将计算结果同人类的主观判断进行了比较,验证了该方法的有效性。该研究工作可以在面向Web的知识检索领域中得到应用,还可以为本体的相关研究提供一定的理论基础。